Marked Microevolution of a Unique Mycobacteriumtuberculosis Strain in 17 Years of Ongoing Transmissionin a High Risk PopulationCarolina Mehaffy1,2*¤, Jennifer L. Guthrie1, David C. Alexander3, Rebecca Stuart4, Elizabeth Rea4,

Frances B. Jamieson1,2

1 Public Health Ontario, Toronto, Canada, 2 University of Toronto, Toronto, Canada, 3 Saskatchewan Disease Control Laboratory, Regina, Canada, 4 Toronto Public Health,

Toronto, Canada

Abstract

The transmission and persistence of Mycobacterium tuberculosis within high risk populations is a threat to tuberculosis (TB)control. In the current study, we used whole genome sequencing (WGS) to decipher the transmission dynamics andmicroevolution of M. tuberculosis ON-A, an endemic strain responsible for an ongoing outbreak of TB in an urban homeless/under-housed population. Sixty-one M. tuberculosis isolates representing 57 TB cases from 1997 to 2013 were subjected toWGS. Sequencing data was integrated with available epidemiological information and analyzed to determine how the M.tuberculosis ON-A strain has evolved during almost two decades of active transmission. WGS offers higher discriminatorypower than traditional genotyping techniques, dividing the M. tuberculosis ON-A strain into 6 sub-clusters, each defined byunique single nucleotide polymorphism profiles. One sub-cluster, designated ON-ANM (Natural Mutant; 26 isolates from 24cases) was also defined by a large, 15 kb genomic deletion. WGS analysis reveals the existence of multiple transmissionchains within the same population/setting. Our results help validate the utility of WGS as a powerful tool for identifyinggenomic changes and adaptation of M. tuberculosis.

Citation: Mehaffy C, Guthrie JL, Alexander DC, Stuart R, Rea E, et al. (2014) Marked Microevolution of a Unique Mycobacterium tuberculosis Strain in 17 Years ofOngoing Transmission in a High Risk Population. PLoS ONE 9(11): e112928. doi:10.1371/journal.pone.0112928

Editor: Igor Mokrousov, St. Petersburg Pasteur Institute, Russian Federation

Received June 11, 2014; Accepted August 22, 2014; Published November 18, 2014

Copyright: � 2014 Mehaffy et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. All the sequencing dataassociated with this manuscript has now been released by NCBI. The accession number for the entire study is: SRP046976. Epidemiological data from PublicHealth Ontario may be available for researchers who meet the criteria for access to confidential data as determined by Public Health Ontario policies and PublicHealth Ontario Ethics Review Board. For requests, please contact Dr. Frances Jamieson, 81 Resources Road, Toronto, ON M9P 3T1, frances.jamieson@oahpp.ca.

Funding: This study was supported by a grant-in-aid from The Lung Association/Ontario Thoracic Society (http://www.on.lung.ca) (CM, FBJ). The funders had norole in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* Email: Carolina.mehaffy@colostate.edu

¤ Current address: Colorado State University, Fort Collins, Colorado, United States of America

Introduction

Globally, tuberculosis (TB) is an important cause of morbidity

and mortality. World-wide, M. tuberculosis is responsible for more

than 1 million deaths per year. In low incidence countries,

homeless/under-housed individuals represent one of the groups at

greater risk for TB infection and disease [1–5]. TB outbreaks

within homeless settings have been documented throughout North

America [6–9].

One endemic strain, designated Ontario A (ON-A), has been

circulating since at least 1997 in the urban homeless/under-

housed population of Toronto, Canada and has been responsible

for TB outbreaks in 2001 and 2004 with new cases identified every

year [5,10]. Genotyping is an essential component of epidemio-

logical investigations. However, the M. tuberculosis ON-A isolates

are defined by a unique combined spoligotype and 24-locus

MIRU-VNTR (24-MIRU) profile, while IS6110 RFLP generates

pseudo-clusters among these strains [10].

Although several reports have highlighted the epidemiological

value of whole genome sequencing (WGS) over traditional

genotyping techniques [11–17], presently only a few studies have

evaluated the utility of WGS to study whole genome changes of M.tuberculosis during long-term continuous active transmission [15].

We evaluated the usefulness of WGS to retrospectively validate

and identify transmission events associated with TB cases due to

M. tuberculosis ON-A over the last 17 years. We used a

phylogenetic analysis based on single nucleotide polymorphisms

(SNP) to portray the microevolution of this M. tuberculosis strain

during almost two decades of on-going transmission in a high risk

population. Our analysis revealed the presence of six independent

transmission chains and the presence of an ON-A natural mutant,

defined by a large genomic deletion that most likely emerged

during the first ON-A TB outbreak in 2001.

Methods

M. tuberculosis clinical isolatesM. tuberculosis isolates were obtained from clinical specimens

routinely received at the Public Health Ontario Laboratories for

TB diagnosis. All available isolates from 1997–2013 with

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genotypes consistent with the ON-A strain [6,18] were selected.

All isolates were susceptible to all first line drugs.

The work described in this manuscript relates directly to

improvement of routine TB surveillance and outbreak manage-

ment, therefore research ethics board (REB) approval was not

required.

DNA extractionGenomic DNA (gDNA) was extracted as previously described

[19] with minor modifications [20].

Genotyping24-locus MIRU-VNTR [21], spoligotyping [22] and IS6110

RFLP [23] were performed using standard methods, and data

were analyzed with BioNumerics v6.1 (Applied Maths, St-Martin

Latem, Belgium). RFLP patterns were compared as previously

described [10].

Whole genome sequencingDNA was prepared for sequencing as described elsewhere [20].

Illumina paired-end reads were trimmed using quality scores and

then aligned to M. tuberculosis H37Rv reference genome

(NC_000962.2) using the CLC Genomics workbench (v.6.0.2)

software. For 5 of the 61 isolates, quality and/or quantity of the

DNA were not suitable for WGS and therefore these were not

included in the analysis.

Accuracy of WGS assembly and analysis workflow was assessed

by sequencing the H37Rv reference strain that is used in our

clinical lab (Material S1).

Variant callingSingle nucleotide polymorphisms (SNPs) and small insertion-

deletion (indel) events were identified using a probabilistic variant

detection with cutoffs of a minimum read depth of 20X and a

variant frequency of at least 75. Indels were not considered for any

further analyses. SNPs were further filtered by removing positions

associated with PE, PPE and PE_PGRS gene families which have

been previously shown to represent false positives and due to their

high variation are not suited for phylogenetic analysis [17]. SNPs

unique to any of the fifty-six high quality whole genome sequences

were manually inspected in each individual alignment for accuracy

and all ambiguous results were discarded.

Phylogenetic analysisA concatemer of the SNPs was generated and then used to

reconstruct the phylogeny of ON-A using SplitsTree v.4 software

[24] and the BioNJ algorithm [25]. Trees were then re-constructed

using the Equal Angle algorithm [26] with equal-daylight and box

opening optimization [27] available in SplitsTree v.4 (Figure 1).

Demographic and clinical dataDemographic characteristics and clinical information for each

TB case was obtained from Ontario’s integrated Public Health

Information System (iPHIS) as well as responsible Public Health

Units (Table 1). This information is routinely recorded by Public

Health Units for all laboratory and clinically confirmed TB cases.

Data was anonymized and all personal information removed from

the final data set. Time lapse between onset of symptoms and

diagnosis as well as treatment start date were not available for most

cases and therefore were not included in this study.

Social network visualizationKnown epidemiological/social connections identified during

routine public health contact investigation (i.e roommates, close

friends, used same drop-in, etc.) among forty-seven ON-A cases

were available in iPHIS. The igraph [28] package of R (v3.0.2)

was used to generate the analysis. Transmission events were

defined based on the genomic and epidemiological information

(i.e. SNP pattern, contact information, year of diagnosis and

infectiousness based on smear and chest x-ray results).

Results

In this study we performed the whole genome sequencing of 61

M. tuberculosis isolates identified during routine genotyping as

members of a large cluster denominated ON-A and spanning 17

years (1998–2013). The 61 isolates corresponded to 57 patients,

most of whom were homeless/under-housed individuals (75.5%).

The epidemiological and molecular typing characteristics of this

cluster up to 2008 have been published elsewhere [5,6]. Table 1

summarizes the demographic characteristics of all 57 patients.

Isolates in this cluster are characterized by a highly similar

24MIRU and spoligotype patterns while RFLP analysis of the

ON-A strain results into three pseudo-clusters defined by RFLP

types A, B and C. The main 24MIRU pattern has not been

reported in the international MIRU database (http://www.miru-

vntrplus.org/MIRU/index.faces) and personal communication

with the corresponding health authorities of the other Canadian

provinces indicates that this genotype is unique to Toronto’s inner

city population.

Of the 61 M. tuberculosis isolates, WGS was successfully

performed in 56 isolates, corresponding to 53 TB cases and further

sub-divided the large ON-A group into 6 different sub-clusters

(SC1-6) (Figure 1 and 2).

Figure 3 combines the RFLP and SNP-clustering results for all

53 cases with final WGS data. Most isolates belong to RFLP type

A (n = 34) followed by RFLP B (n = 5) and RFLP C (n = 3). Nine

isolates were not clustered by RFLP and 1 presented a mixed

pattern corresponding to an infection with strain ON-A and strain

ON-B, which is also commonly found in the homeless/under-

housed population. Figure 3 illustrates the lack of correlation

between RFLP and SNP-clustering with the exception of RFLP

type B which corresponded to SC4.

Sub-cluster classification was based on SNP analysis using as

reference, the genome of laboratory strain H37Rv. In summary,

722 SNPs were identified in ON-A isolates when compared with

H37Rv. Of these, 641 SNPs, including 333 (52%) non-synony-

mous, 224 (35%) synonymous, and 84 (13%) non-coding SNPs,

were conserved in all sequenced isolates and only served to

differentiate H37Rv from the ON-A strain. The remaining 81

SNPs were variable among the sequenced isolates and were used

to identify sub-cluster associations.

Most isolates belonged to the most recently emerged sub-cluster

SC6 (n = 24), followed by SC4 (n = 10), SC1 (n = 8), SC5 (n = 5)

and SC2 (n = 2). One isolate was not placed in any of the 6 sub-

clusters.

Of the 81 SNPs, 2 were present in the majority of ON-A

isolates, except for most isolates in SC1. One SNP was only

present in SC-6 and one was present in both SC4 and SC5

isolates. The remaining 77 SNPs were either sub-cluster associated

(i.e. present in two or more isolates) (17/77) or singletons (60/77).

Sub-clusters, SNPs and transmission eventsPhylogenetic analysis identified 6 ON-A sub-clusters (Figure 1).

One isolate did not group with any of the identified sub-clusters.

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Phylogenetic analysis and temporal association of isolates from the

ON-A strain suggests the independent emergence of SC1–SC5

prior to 1997 and subsequent clonal expansion of sub-clusters

SC3, SC4 and SC5 (Figures 1 and 2). Contrary to this, SC6

appears to have emerged in 2001 during the first TB outbreak

associated with ON-A.

SNP patterns of isolates belonging to SC1 group suggest they

represent infections acquired from a common ancestor(s) prior to

1997 and most likely represent re-activation of latent TB infection

rather than immediate secondary cases. The exceptions were two

isolates, (WT24-07 and WT27-08) with SNP patterns identical to

the source isolate WT23-07. The two individuals, from whom the

isolates WT23-07 and WT24-07 were obtained, lived in the same

rooming house, and both used the same drop-in location which

was associated with the two ON-A TB outbreaks. Interestingly, the

individual associated with isolate WT27-08 was diagnosed in a

different jurisdiction north of Toronto, but is clearly related to the

other two isolates and demonstrates the high mobility of this

population.

Contrary to SC1, all other sub-clusters had a clear source case.

In group SC2, isolate WT5-00 gained 4 SNPs in approximately 2

years after infection from WT3-98. In group SC3, the source case

(WT07-01) resulted in two other primary TB cases in the same

year and a later reactivation case in 2004.

In SC4, the source case (WT17-04) resulted in secondary cases

in shelter workers (both in 2004). Five additional cases with an

identical SNP pattern as the suspected source case were also

identified in subsequent years (2005–2010). In addition, the source

was also genetically related to a case in 2006 with 1 SNP gained

and another case in 2010 with 4 additional SNPs. It is not possible

to determine if the case in 2010 (WT29-10) originated from the

source case or from one of the five cases with identical SNP

pattern to the source. However, the large number of SNPs suggests

that WT29-10 corresponds to a re-activation from an infection

acquired in 2004 or 2005. This is also supported by epidemio-

logical data that indicates WT29-10 may have shared a common

workplace with WT17-04 at the time this source case was ill.

24-MIRU, spoligotype and RFLP indicated that A/B-01

represented a mixed infection with two strains commonly found

in this population (ON-A and ON-B) [6] and therefore its WGS

data was not included in the phylogenetic analysis. However,

manual inspection of all 81 variable SNP positions in the genome

alignment generated for this isolate demonstrated the presence of

all SC5 SNPs, allowing us to include the A/B-01 case within the

SC5 group.

The two suspected source cases in group SC5 (WT6-01 and A/

B-01) resulted in two subsequent cases in 2004 and 2007, both of

which have identical SNP patterns to the suspected source(s). In

addition, these two cases were the most likely source for the

emergence of SC6 in 2001.

It is possible that the high bacteria burden present in WT6_01

or the A/B-01 mixed case (smear 2+ and 3+ respectively)

contributed to the emergence of SC6. Both patients also had

abnormal chest x-rays and together with their smear results

Figure 1. ON-A Phylogenetic tree constructed in Splitstree ver. 4. All 722 polymorphic sites were included. Phylogenetic tree was built usingBioNJ algorithm and then filtered for parsimony-informative sites.doi:10.1371/journal.pone.0112928.g001

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suggest they were highly infectious. The chest x-ray of the mixed

A/B case also showed cavitation. Social network analysis

demonstrated the large number of contacts shared by these two

patients, including individuals in both ON-AWT and ON-ANM

groups (Figure S1).

The SC6 group was very homogeneous. SNPs within this group

were mostly singletons and represented temporal acquisition of

substitutions in single isolates. Nine SC6 isolates (37.5%) had no

accumulation of SNPs in an 8 year time frame (Figure 2).

Because of the high number of isolates with identical SNP

patterns in SC6, it was not possible to determine the exact

transmission chain in this group. Multiple control measures to

reduce spread of TB in Toronto’s homeless shelters were

implemented as a consequence of the outbreaks in 2001 and

2004. This provides support to the idea that most cases after 2005

correspond to reactivation of infections acquired during one of the

two TB outbreaks. The only exception is case NM17-08 which is

an individual outside of the high risk group for which contact

investigations indicate the source case for that individual was

NM15-06. For the remaining cases, the most likely index case was

NM7-01 who was highly infectious, with smear +3 and abnormal

chest x-ray.

WGS revealed a large genomic deletion in SC6We discovered a large genomic deletion of more than 15 kb,

comprising 12 genes in 26/61 (43.5%) ON-A isolates (Table 2),

dividing the ON-A strain into two groups, ON-AWT and ON-ANM

representing presence or absence of the 15 kb genomic region,

respectively (Figure 1, Table 2). SC1–SC5 belong to the ON-AWT

group while isolates in SC6 are all ON-ANM. In contrast to ON-

ANM, the ON-AWT variant was very heterogeneous, characterized

by the presence of multiple sub-clusters. In addition, thirty-two

ON-AWT SNPs were singletons while 16 were sub-cluster

associated. ON-AWT isolates also presented 3 additional SNPs

within the deletion region. These SNPs were not included in the

phylogenetic analysis. The genomic deletion was confirmed by a

custom PCR in all isolates, including 5 for which WGS data was

not available (Material S1). Sanger sequencing of the ON-ANM

PCR amplicons demonstrated that the insertion sequence IS6110is present and flanked by an incomplete Rv1358 at the 59-end and

by an incomplete Rv1371 at the 39-end. Sanger sequencing of the

flanking areas of the 15 kb deletion region in ON-AWT also

confirmed the presence of IS6110 at the 39 end of the region

(upstream of Rv1371).

Heterogeneity resulting from IS6110-mediated deletion events

during active TB infection has previously been reported in an

individual with a very high bacteria burden [29] and it is possible

that the high bacteria burden present in the two possible source

Table 1. Demographic characteristics of ON-A TB cases.

Variable All Individuals (n = 57) ON-AWT Individuals (n = 33) ON-ANM Individuals (n = 24)

Age (years)

Mean age (6 std dev) 50.0 (612.9) 51.4 (611.6) 48.2 (614.4)

Range 20.3–74.3 23.8–74.3 20.3–72.5

Gender

Female 5 3 2

Male 52 30 22

Birthplace

Born in Canada 40 22 18

Born outside Canada 11 5 6

Unknown 6 6 0

Disease Classification

Pulmonary 49 27 22

Extra-pulmonary 2 1 1

Pulmonary with extra-pulmonary involvement 6 5 1

Mortality (TB as cause of deathy)

Yes 3 3 0

No 10 5 5

Unknown 6 5 1

Housing Status

Under-housed 43 23 20

Housed 4 2 2

Unknown 10 8 2

Risk Factors

HIV/AIDS 8 4 4

Injectable drug use 10 4 6

Alcohol abuse 31 17 14

y Number of deaths during or shortly after completing TB treatment.doi:10.1371/journal.pone.0112928.t001

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cases (WT6-01 and A/B-01) as described previously could have

contributed to the emergence of the deletion and origin of the ON-

ANM variant.

Discussion

Microevolution and adaptation events of M. tuberculosisduring active TB transmission

We analyzed a cluster of M. tuberculosis isolates (ON-A)

associated with on-going TB transmission in a large urban setting

and its inner-city homeless/under-housed population. In order to

evaluate the evolution of this M. tuberculosis strain during a 17

year period, we performed WGS analysis for all available ON-A

isolates. We discovered a large genomic deletion (.15 kb) in a

subset of ON-A isolates that divided the ON-A strain into two

strain variants (ON-AWT and ON-ANM). This large genomic

deletion, which is present in 43% of the ON-A isolates

demonstrates the presence of two distinct groups independent of

the RFLP pattern.

Our phylogenetic and epidemiological analyses suggest this

deletion emerged in 2000 during the first TB outbreak caused by

ON-A.

The 15 kb deletion comprises a cluster of 12 genes, of which at

least 5 code for regulatory proteins. Regulatory proteins govern

the expression of clusters of genes involved in specific molecular

pathways and therefore are ultimately responsible for the ability of

the cell to adapt and survive in new environments. Two of the

deleted genes identified in the ON-ANM correspond to regulators

Figure 2. Transmission events of ON-A TB cases based on WGS information and epidemiologic analysis. Colored solid circles representTB isolates that are identical to the suspected primary case (0 SNPs). Open circles represent related cases, separated by black solid dots representingSNPs acquired over time. Triangles represent TB cases outside of the homeless/under-housed group. Isolates from the same TB patient arerepresented by circles with dots in their background: WT25-08 and WT26-08; NM2-01 and NM3-01; and NM17-08 and NM19-08. *Isolates that hadmore than one passage prior to WGS.doi:10.1371/journal.pone.0112928.g002

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of the Sigma-factor F (SigF). Sigma factors bind to RNA

polymerase and alter its promoter preference resulting in subsets

of genes that are differentially expressed upon environmental

stresses to which a particular sigma factor responds. M.tuberculosis SigF is induced by nutrient starvation [30] and it is

involved in the late stages of the disease [31]. One of the anti-sigF

deleted genes (Rv1364c) strongly interacts with sigF and also with

the sigF antagonist RsbW [32]. Other regulatory genes identified

in the 15 kb deletion are the LuxR family regulator (Rv1358)

which is also regulated by SigF [33], a signal transduction

regulatory gene (Rv1359) and a conserved gene (Rv1366) in which

we identified a domain representative of RelA-SpoT superfamily,

responsible for the regulation of ppGpp concentration. ppGpp is a

modified nucleotide used by bacteria as intracellular messengers

that respond to different environmental stresses [34,35]. In M.tuberculosis, ppGpp responses are associated with virulence and

long-term survival [35,36].

The presence of the insertion sequence IS6110 at the site of the

deletion suggests this event was IS6110 mediated. Reports

elsewhere have shown that IS6110 mediated deletion events are

common in M. tuberculosis [29,37,38] and even though the

identification of a large genomic deletion in this highly related M.tuberculosis strain is very interesting, it should not be surprising

given the high IS6110 mediated mutation rate (transposition/

generation) [39]. It has been proposed that although deleterious

IS6110 mediated mutations may occur frequently, these mutants

are rapidly purged from the population by purifying selection [39].

Contrary to this, the 15 kb deletion seems to have emerged during

the first TB outbreak in 2001; and since then the ON-ANM has

established itself among the homeless/under-housed population in

Toronto.

Other examples of non-deleterious deletions exist. An M.tuberculosis isolate from the CAS family bearing a chromosomal

deletion, was responsible for a large TB outbreak in Leicester, UK

and was shown to have a lower inflammatory phenotype and

higher intracellular growth similar to the hypervirulent strain

HN878 [40]. An IS6110-mediated deletion was also reported

elsewhere in an individual with disseminated TB and a high

bacterial burden [29]. The deleted isogenic strain in this individual

was only present in the lymph nodes and although transmission

events were not identified, it is clear that the deletion did not

impair the bacilli to replicate and cause disease.

Although the physiological effect of the ON-ANM 15 kb deletion

is presently unknown, based on the information available for the

deleted genes, five of which have some role in gene regulation

under environmental stresses; this deletion may have a direct

impact on several molecular pathways and could enhance the

capacity of this variant to spread and cause disease.

Conversely, the 15 kb deletion observed in ON-ANM is in a

hotspot position for IS6110 transposition events [41,42] and the

genes located in this region may not be essential for the bacteria to

transmit and cause disease. This would support the theory that the

ON-ANM was randomly fixed in the homeless/under-housed

population, possibly supported by higher transmission rates in

congregate setting such as shelters and a high prevalence of co-

morbidities). It is possible that a weakened immune system allows

for the appearance of potentially ‘‘unfit’’ mutations or large

deletion events such as those reported here.

Studies focusing on fitness assessment and mining of the

proteome and transcriptome of ON-A variants to determine the

role of the deleted genes in the physiology and virulence of M.tuberculosis are currently underway.

WGS as tool to determine TB transmission dynamicsAlthough RFLP, MIRU-VNTR and spoligotype are all

powerful genotyping techniques, they cannot establish chronology

of infection and lack resolution in long-term transmission events

[43–46]. In contrast WGS has the potential for resolving the

transmission dynamics of TB infection and when complemented

with epidemiological data, it is an excellent tool to identify

Figure 3. Dendrogram of IS6110 RFLP showing the 15 kbdeletion distribution. Branches indicate the clusters with identicalRFLP patterns. Colored squares represent each of the six sub-clustersidentified by WGS. Information regarding the presence or absence ofthe 15 kb deletion for each isolate is shown as ‘‘deleted’’ or ‘‘intact’’.doi:10.1371/journal.pone.0112928.g003

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individual transmission events, and its use is very valuable in

highly mobile communities such as the homeless/under-housed.

In addition to providing relevant and detailed information that

can be used to construct and/or validate the transmission

dynamics of a given strain, WGS also provides important

information regarding SNPs, indels and larger genomic structural

variants that can provide insights into the physiological charac-

teristics of M. tuberculosis as a whole as well as particular

characteristics of the strain of interest.

Recent genomics studies have highlighted the benefits of whole

genome sequencing over traditional genotyping to uncover the

transmission dynamics and molecular-guided TB control and

surveillance [11,12,14–17]. For instance, Gardy et al. [12]

demonstrated the usefulness of overlapping WGS data and social

network analyses during outbreak investigations. The authors were

able to determine transmission dynamics of 32 M. tuberculosisoutbreak isolates in a period of two and half years. Similar to our

findings, their SNP analysis revealed two lineages suggesting not

one, but two simultaneous chains of transmission. The SNP calling

algorithm in Gardy’s study did not include filtering out PE/PPE

genes. This resulted in a higher genetic diversity than expected,

but it is remarkable the similar findings with our study vis-a-vis the

identification of multiple strain variants and clusters with otherwise

identical RFLP and MIRU-VNTR patterns in a high risk

population. Unlike the study by Gardy et al. [12] in which the

two strain variants diverged and circulated in the community well

before the outbreak, the ON-AWT variant appears to have evolved

during the 2001 TB outbreak and the emergent ON-ANM was

rapidly fixed in Toronto’s homeless/under-housed population.

Most WGS studies related to TB transmission dynamics have

been focused on TB outbreaks in short time frames (1–30 months)

[11–14,16]. In all cases WGS represented an invaluable tool to

either support or complement directionality of transmission based

on epidemiological contact studies. A few long-term studies have

also been conducted. Roetzer et al. [17] performed a prospective

study to evaluate the correlation of WGS analyses with contact

tracing data. Eighty-six M. tuberculosis isolates from a 13 year

time frame were sequenced. WGS analysis confirmed the clonal

expansion of an outbreak strain that was initially seen in a specific

social setting in Hamburg, Germany, but slowly spreading outside

of this particular setting [17]. In contrast to our findings, SNPs

were the primary source of genome evolution during transmission

and all small deletions and insertions detected were constant in all

isolates.

Walker et al. [15] also confirmed the high power and resolution

provided by WGS and determined the genomic diversity between

patients in MIRU-VNTR community clusters in a period

spanning 1–12 years.

Here, we studied the dynamics of TB disease over 17 years of

on-going transmission in a homeless/under-housed population.

Previous studies have demonstrated that large deletions and

hundreds of polymorphic sites can exist in isolates with identical

IS6110 and MIRU-VNTR patterns [15]. As expected, we

confirmed that clusters obtained by IS6110 RFLP typing were

not in agreement with the WGS-based division of ON-A isolates

into two major groups based on the 15 kb large genomic deletion).

In addition, we identified 81 SNPs that are variable within the

ON-A strain and further subdivide the strain into 6 distinct sub-

clusters.

The large heterogeneity observed in our study group resulted in

an average of 1.5 SNPs/case (81 SNPs/55 M. tuberculosis isolates)

is slightly larger than those reported in the majority of M.tuberculosis-WGS studies which ranged from 0–1.3. However, in

those studies, the larger variation rates were associated to

community clusters (as opposed to household groups) spanning

more than 10 yr of transmission [13,15,17]. Only two studies have

Table 2. Genes present in the 15 kb deleted region.

Deletedgene Function Annotations Reference

Rv1358 Probable transcriptionalregulatory protein

LuxR family signature, expressionregulated by SigF; deleted in someclinical isolates

[33,49]

Rv1359 Probable signaltransductionregulatory protein

Adenylate cyclase, family 3(Sensory pathways)

Rv1360 Probable oxidoreductase Expressed during GP infections;mRNA down-regulated in starvation

[50,51]

Rv1361 PPE19 - Unknown Expressed during GP infections;mRNA down-regulated instarvation; regulated by PhoP

[50–52]

Rv1362c Unknown Expressed during GP infections;membrane protein

[50]

Rv1363c Unknown None

Rv1364c Possible sigma factor Fregulatory protein

May be a capreomycin target;responds to heat stress

[53]

Rv1365 Anti-anti sigma factor F Regulated by Redox potential

Rv1366 Unknown Homology to RelA-SpoTsuperfamily (ppGpp synthesis)

Rv1367c Possibly involved in cellwall metabolism

Homology to PBPs, B-lactamases

Rv1368 Lipoprotein None

Rv1371 Unknown Probably conserved membrane protein

doi:10.1371/journal.pone.0112928.t002

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shown larger variability. Cluster 9 in the study reported in Walker

et al. (2013) [15] which was associated with substance abuse and

presented an average of 2 SNPs/case in a 9 yr span; and the large

cluster in Gardy et al. (2011) with a rate of more than 5 SNPs/case

likely due to the inclusion of hypervariable PPE/PE genes [12].

Our rate of 1.5 SNPs/case may be a result from the long time

span as well as the likely possibility of on-going transmission prior

to 1997 and thus potential for missed links/cases. Although

information regarding delay in diagnosis and adherence to

treatment was not available, these are conditions likely to be

encountered in a high risk setting such as the homeless/under-

housed and could increase the possibility of bacilli evolution. For

instance, SNP patterns of isolates belonging to SC1 group suggest

they all represent infections acquired from a common ancestor

prior to 1997 and most likely represent re-activation of latent TB

infection with independently gained substitutions during latency

ranging from 3–7 SNPs per event.

This hypothesis is supported by a study of TB latency in

macaques that suggests M. tuberculosis mutation rate is fairly

similar in active and latent TB, with the rate in latent TB being

slightly higher [47]. In contrast, a recent study of latency in

humans suggests a lower mutation rate during latency when

compared to active TB [48]. However, this study only included

two subjects with a long period (.10 yr) of latent infection, and as

the authors suggested, it is possible that a broader spectrum of

mutation rates exists during latent TB.

Certainly, our study supports this idea, as we were able to show

both high heterogeneity as observed in SC1, but also several cases

with a low SNP fixation (0–2 SNPs/yr) in the other ON-A groups

particularly SC4–SC6 in which latency periods of more than 5

years and zero SNP changes were observed. For instance, in the

ON-ANM (SC6), some of the isolates obtained in 2008 did not

present any additional SNPs when compared to isolates obtained

in 2001. Similarly, group SC4 of ON-AWT was represented by

identical isolates from 2004 to 2010 and SC5 included isolates with

identical SNP pattern from 2001 to 2007.

Although most SC1 cases are probably reactivations, we

strongly believe that the high heterogeneity is not only due to

latency but also to missing links within the group. This could be

due to unrecognized/undiagnosed TB cases, TB cases ultimately

diagnosed outside the province of Ontario, or to TB cases

diagnosed prior to 1997 for which no isolates are available. Gaps

in the PHOL genotyping database are another factor. Our

laboratory implemented IS6110 RFLP in 1997 but, until the

introduction of a semi-automated MIRU-VNTR and spoligotyp-

ing program in 2007, Ontario did not have universal genotyping

and only a subset of strains were analyzed or archived. Currently,

the PHOL database includes patterns for .4000 isolates, but data

for hundreds of strains obtained between 1997 and 2007 were

never obtained.

In summary, the large number of singletons observed in our

study, and the absence of progenitors for some SCs, are most likely

due to gaps in our strain collection and genotyping database.

However, SNPs present in some isolates may represent substitu-

tions that originated during latency. In group SC6, the large

majority of cases resulted from the presence of a super-spreader

(NM7-01), and this suggestion is supported by clinical findings (e.g.

smear 3+ and abnormal chest x-rays) consistent with a highly

infectious state. Although it is possible that the high bacterial

burden in this case could have contributed to the emergence and

spread of isolates with differences of 2–4 SNPs, the large

heterogeneity observed in these isolates is also compatible with a

more plausible explanation such as mutations originating after

secondary infection and long latency periods.

Conclusion

We performed a large retrospective and longitudinal study

which resulted in the characterization of the microevolution of a

unique M. tuberculosis strain associated with a high-risk group

population. Despite a clustered genotype pattern based on the

combination of 24-MIRU and spoligotype, we identified a large

genomic deletion in nearly half of the sequenced isolates and

were able to identify the emergence of this deletion event to

have occurred during the first TB outbreak caused by ON-A in

2001. Even though loss of large genomic regions is a major

source of variation in M. tuberculosis [49], to the best of our

knowledge, this is the first study in which identification of such a

region was pinpointed during active TB transmission. Further-

more, we identified a larger than expected heterogeneity

resulting from the microevolution of ON-A in 17 years of

transmission and further delineated this strain into 6 distinct

sub-clusters.

WGS has been proposed as a ‘‘gold standard’’ for strain typing

in M. tuberculosis [12,13,15]. Our study confirms the value of

WGS to determine transmission dynamics and isolate relatedness

in a large cluster of on-going TB transmission extending over

many years in a high risk population. Nonetheless, the large

number of shared contacts between TB cases, the mobility of

homeless/under-housed individuals, and TB latency contributed

to the complexity of determining individual events of TB

transmission in this population.

The peculiarities of our study cohort are noticeable: high risk

cases, on-going transmission despite control measures, high

mobility of cases and likely missing links. All of these factors could

be associated with a higher than average microevolution dynamic

resulting in high variability and multiple transmission chains. Our

intention is not to make general conclusions adapted to other

transmission environments but to decipher the high clonal

complexity and microevolution rates that could be expected in a

transmission event in a complex population.

Supporting Information

Figure S1 Social network analysis of ON-A subjects.Social network analysis was performed using R statistical software

(v3.0.2) with the igraph package. Each large circle represents a

single individual colored by their sub-cluster as determined by

WGS SNP analysis. Grey lines represent common contacts

between study individuals and thick black lines represent direct

epidemiological/social-connections between study individuals.

(PDF)

Material S1 Supplementary methods and results sec-tion. The supplementary methods describe the PCR amplifica-

tion of regions flanking the 15 kb deletion. The supplementary

results describe the WGS results of our laboratory strain H37Rv

and how these results were use to evaluate the accuracy of our

SNP calling algorithm. Table S1 in Material S1 shows the primers

used for PCR amplification of regions flanking the 15 kb deletion.

(DOCX)

Acknowledgments

We would like to thank the Public Health Ontario TB and Mycobacter-

iology Laboratory and research staff, responsible for the initial isolation,

cultivation, and susceptibility testing of the clinical isolates used in the

study, deletion-PCR and genotyping.

Microevolution of an Endemic M. tuberculosis Strain

PLOS ONE | www.plosone.org 8 November 2014 | Volume 9 | Issue 11 | e112928

Author Contributions

Conceived and designed the experiments: CM FBJ DCA. Performed the

experiments: CM JLG. Analyzed the data: CM DCA JLG RS ER.

Contributed reagents/materials/analysis tools: CM FBJ. Contributed to

the writing of the manuscript: CM. Review and edit the manuscript: JLG

DCA RS ER FBJ.

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